TWI579787B - Systems and methods for instant e-coupon distribution - Google Patents

Description

Instant electronic coupon distribution system and method

In general Internet commerce, online sales promotions are often used to provide consumers with short-term buying incentives. Typical sales promotions include discounts, samples, competitive promotions, loyalty rewards, and coupons. A coupon is a voucher that enables the holder to enjoy the recorded discount for the purchase of a particular product or product package. Manufacturers and sellers will issue electronic coupons over the Internet and distribute electronic coupons directly to users on a web page viewed by web users.

In order to identify a potential buyer, an online store owner and/or platform provider generally collects web user history data of the web user and predicts the data through a batch allocation model. The current purchase intent of the web user. Once a web page user is found to have an intent to make a purchase, the manufacturer or vendor will provide the web user with a long or medium term product coupon.

However, in fact, an online store owner or platform provider may find it difficult to assign a model based on the batch, providing a commercially viable electronic coupon distribution service for the manufacturer or vendor. For example, the implementation of the batch allocation model is complex. A basic implementation of the batch allocation model may require many components, such as data warehousing, complex data mining algorithms, type comparison services, electronic coupon databases, performance returns and monitoring systems. For a line It may be difficult for the store owner or platform provider to provide the described system.

In addition, online store owners and/or platform providers may often find that the association between historical data exploration of a web user and current purchase intent is too low to produce an accurate prediction. In order to identify the buyer, the current implementation of the batch allocation model requires the online store owner or platform provider to perform historical data mining in batches to predict the purchase intention of the web user. Because in the data mining calculation, the correlation between multiple types of data in the exploration data is a common and difficult problem, the online store owner and/or platform provider often finds it difficult to perform data type comparison. Get useful information, especially when making timely type comparisons. In addition, a web page user often loses interest in a particular product or has purchased the product before its historical data reflects the purchase intention of the web user. For these reasons, the above data mining strategy may result in a low allocation efficiency, and/or electronic coupons may not be delivered to the web user in a timely manner.

100‧‧‧Network

101‧‧‧Client device

102‧‧‧Mobile devices

103‧‧‧Mobile devices

104‧‧‧Mobile devices

105‧‧‧Mobile devices

106‧‧‧Search server

107‧‧‧Content Server

108‧‧‧Wired connection

109‧‧‧Wireless connection

200‧‧‧Client device

222‧‧‧Central Processing Unit

223‧‧‧ random access memory

224‧‧ ‧ busbar

226‧‧‧Power supply

234‧‧‧Read-only memory

240‧‧‧Basic input and output system

241‧‧‧ operating system

242‧‧‧Application

243‧‧‧Send application

244‧‧‧Data storage

245‧‧‧ browser

250‧‧‧Internet interface

252‧‧‧ audio interface

254‧‧‧ display

256‧‧‧Keyboard/Keyboard

258‧‧‧ illuminator

260‧‧‧Input/output interface

262‧‧‧Tactile interface

264‧‧‧Global Positioning System

300‧‧‧Server

322‧‧‧Central Processing Unit

324‧‧ ‧ busbar

326‧‧‧Power supply

330‧‧‧Media

332‧‧‧ memory

341‧‧‧ operating system

342‧‧‧Application

344‧‧‧Data storage

350‧‧‧Network interface

352‧‧‧Audio interface

354‧‧‧ display

356‧‧‧Keypad/Keyboard

358‧‧‧Input/output interface

400‧‧‧Online Electronic Coupon Distribution System

410‧‧‧External Website

412‧‧ Search website

414‧‧‧Related products

416‧‧ social networking sites

420‧‧‧ Target website

422‧‧‧Product Inquiry Page

424‧‧‧ watch list

426‧‧‧Product page

428‧‧‧Shopping cart

429‧‧‧Product Search

430‧‧‧Backdoor server

432‧‧‧Product Inquiry Database

434‧‧‧ Watch List Database

436‧‧‧Product Database

438‧‧‧Shopping cart transaction database

440‧‧‧Yahoo! grid

442‧‧‧Search records

444‧‧‧Browse records

450‧‧‧Online Electronic Coupon Distribution System

452‧‧ ‧ index pre-processing unit

454‧‧‧ Parcel Index

456‧‧‧Target server

458‧‧‧Electronic coupons

470‧‧‧Client device

500‧‧‧ method

510‧‧ steps

520‧‧‧Steps

530‧‧‧Steps

540‧‧‧Steps

550‧‧ steps

560‧‧ steps

A better understanding of the systems and methods described herein can be obtained by reference to the following drawings and description. Non-limiting and non-exhaustive embodiments are described with reference to the following figures. The elements in the figures are not necessarily to scale, but rather are emphasized on the basis of describing the principles of the invention. In the drawings, like reference numerals indicate corresponding parts in the different drawings.

The first figure is a schematic diagram of an example network environment; the second figure is a schematic diagram of an example client device; the third figure is a schematic diagram of an example server; and the fourth figure is an embodiment of an online electronic coupon distribution system. Schematic diagram; and The fifth figure is a flow chart of an embodiment of a method for distributing an electronic coupon to a web page user.

The present disclosure is directed to a computer-implemented system and method for calculating a purchase intent score for the distribution of online electronic coupons. The purchase intent score can reflect the extent to which a web page user wants to purchase a target product. In some embodiments, the purchase intent score is simultaneously incorporated into both the historical purchase intent information and the instant purchase intent information of a web page user.

I. Example network environment

The first figure is a schematic diagram of an example network environment in which a method of calculating a purchase intention score for the distribution of online electronic coupons can be operated. The subject matter of the present invention is also intended to encompass other network environments that can be varied, for example, in terms of configuration or component form. As shown, for example, in the first figure, a network 100 can include various networks, such as the Internet, one or more local area networks (LANs), and/or wide area networks (WANs), wired form connections 108, Wireless form connection 109 or any combination thereof. The network 100 can be connected to a plurality of devices so that communication can be exchanged, such as at multiple servers (e.g., content server 107 and search server 106) and multiple client devices (e.g., client device 101- The connection between the 105 and the mobile device 102-105) or other device form, such as connecting a plurality of wireless devices over a wireless network. Network 100 may also contain a large number of storage devices, such as network attached storage (NAS), storage area network (SAN), or other forms of computer or machine readable media, for example.

A network can also include any form of implementation that connects to individuals via a communication network or through various secondary networks to transmit/share information. For example, the network can contain content A distribution system, like a peer-to-peer network or a social network. A peer-to-peer network can utilize the computing power or bandwidth of multiple network participants to connect multiple nodes through random placement or configuration, where the nodes act as both a client device and a server. A social network can be a network of individuals, such as acquaintances, friends, family members, colleagues or co-workers, connected through a communication network or through various secondary networks. Potentially, through the communication network or the secondary network, subsequent follow-up may form new relationships because of social interaction. For example, a social network can be utilized to identify additional links to multiple activities, including but not limited to appointments, work networks, receiving or providing service referrals, content sharing, establishing new associations, maintaining existing associations, identifying potential activity partners, and performing Or support commercial transactions and more. A social network can also create associations or links with multiple entities, not just individuals, such as companies, brands, or so-called "virtual people." A personal social network can be expressed in various forms, such as visual, electronic or functional. For example, a "social map" or "social graph" can represent an entity in a social network as a node and present the association as an edge or a link. In general, the network concept in the present application is intended to encompass any conventional or current form of network that facilitates the transmission of information or advertising.

II. Example Network Environment Components

The second figure is a schematic diagram of an example client device that can be used in a network, such as the network shown in the first figure. A client device can include a computing device capable of transmitting or receiving signals, such as through a wired or wireless network. A client device may include, for example, a desktop computer 101 or a portable device 102-105, such as a mobile phone or smart phone 104, a display pager, a radio frequency (RF) device, an infrared (IR) device, and a personal digital device. Assistant (PDA), handheld computer, tablet 105, laptop 102-103, set-top box, wearable computer, integrated device combining various features, such as various features of the aforementioned devices, Or other similar device.

A client device may vary in its capabilities or characteristics, and the subject matter claimed herein is intended to cover a wide range of potential variations. For example, a client device can include a keypad/keyboard 256 or display 254, such as a monochrome liquid crystal (LCD) display for displaying text. In contrast, however, as another example, a web-enabled client device can include one or more physical or virtual keyboards, mass storage, one or more accelerometers, one or more Gyroscopes, Global Positioning System (GPS) 264 or other location-capable capabilities, or highly functional displays, such as touch-sensing 2D or 3D displays.

A client device may include or may execute various operating systems 241, including a personal computer operating system such as Windows, iOS or Linux, or may include a mobile operating system such as iOS, Android or Windows Mobile, or may include any An operating system that is available at the time of filing of the present application or in the future. A client device can include or can execute various possible applications 242, such as browser 245 and/or messaging application 243. A client application 242 can enable communication with other devices, such as one or more messages, such as via email, SMS (MMS) or Multimedia Messaging Service (MMS), including through a network. road, like social networks, for example, include ^{Facebook TM, LinkedIn TM, Twitter TM} , Flickr TM or Google ^TM, these are just some of the possible examples. A client device can also include or execute an application for content communication, such as textual content, multimedia content, or the like. A client device can also include or execute an application to perform various possible tasks, such as browsing, searching, playing various content forms, including native storage or streaming video, or games such as a fantasy sports league. The foregoing is meant to be illustrative, and the subject matter of the invention is intended to cover a wide range of possible features and capabilities.

The third figure is a schematic diagram of an example server that can be used in a network, such as the network described in the first figure. A server 300 can vary widely in configuration or capability, but can include one or more central processing units 322 and memory 332, one or more media 330 that store a plurality of applications 342 or data 344 (like a Or a plurality of mass storage devices), one or more power supplies 326, one or more wired or wireless network interfaces 350, one or more input/output interfaces 358, and/or one or more operating systems 341, such as ^{Windows Server TM, Mac OS X TM} , Unix TM, Linux TM, FreeBSD TM or other similar items. Thus, as an example, a server 300 can include a dedicated rack-mounted server, a desktop computer, a laptop computer, a set-top box, an integrated device that combines various features, such as two or more of the aforementioned devices. Features, or other similar devices. The server 300 can be implemented as a search server 106 or a content server 107. A content server 107 can include a device having a configuration to provide content to another device over a network. For example, a content server 107 may host a Web site, such as a social networking site, examples of which may include ^{Flickr TM, Twitter TM, Facebook TM} , LinkedIn TM person or a user website (such as blogs, video blogs , online dating sites, etc.), but not limited to this. A content server 107 can also host various other websites, including commercial websites, educational websites, dictionary websites, encyclopedia websites, Wikipedia, financial websites, government websites, and the like, but is not limited thereto. A content server 107 can further provide various services including network services, third party services, audio services, video services, email services, instant messaging (IM) services, short message services (SMS), and multimedia messaging services (MMS). , but not limited to, File Transfer Protocol (FTP) services, Voice over Internet Protocol (VOIP) services, calendar services, photo services, or other similar services. Examples of content may include text, pictures, audio, video, or the like, which may be processed, for example, in the form of a physical signal such as a telecommunication signal, or may be stored in a memory, for example, in a physical state. Examples of devices that can operate as a content server include desktop computers, multi-processor systems, microprocessors or programmable consumer electronics, and the like.

When the server 300 transmits a web page to a client device (e.g., the second image of the client device 200) through a network, the server 300 can utilize any technology that is available at the time of filing of the present application or in the future. , identify the client device. For example, the server 300 can pass the name of the client device, the system host name of the client device, the Internet Protocol (IP) address of the client device, the WiFi address of the client device, the client a media access control (MAC) address of the end device, an International Mobile Equipment Identification Number (IMEI) of the client device, an integrated circuit card identifier (ICCID) included in the client device, a subscriber identity card (SIM) , or a small text file stored in the client device, etc., to identify the client device. Accordingly, when a web page user is visiting a web page transmitted by the server 300, the server 300 can perform an online sales promotion for an owner (the web user) of the client device.

III. Exemplary examples

The fourth figure is a schematic diagram of one embodiment of an online electronic coupon distribution system that can be implemented in a network, such as the network 100 shown in the first figure. The online electronic coupon distribution system 450 can include an index pre-processing unit 452 for collecting and/or receiving a history and immediate intention (PI) reflecting a web page user (not shown) with respect to a target product. News. For example, if a web page user has previously purchased a target product, its previous purchase history may reflect that the user may have a historical purchase intent for the target product. Similarly, if the webpage user browses the webpage of the target product more than ten times a day, the browsing history may reflect that the webpage user may have an instant purchase for the target product. Buy intent.

A package index 454 can be used to store information about the intent information of the web page user. The package index 454 can be a data exploration result of the activity data of a web user history line. A target server 456 (eg, a purchase intent engine) can analyze the information related to the purchase intent and provide the web user with an electronic coupon when determining that the web user has a purchase intent greater than a threshold value. 458 is used to purchase the target product.

In some embodiments, the pre-indexing processing unit 452 can be integrated with a target website 420, such as with an online shopping website. When a web page user is browsing the target website 420, a back door server 430 can transmit a plurality of web pages of the target website 420 to the web page user. The backdoor server 430 can be the server 300 in the third figure. In some embodiments, the client device 470 can be the client device 200 in the second figure.

The target website 420 can sell a variety of products and can provide various web pages, functions, toolbars, and/or links to the client device 470. For example, for each product, the target website 420 can provide a product page 426, a corresponding product inquiry (question and answer) page 422, and/or for searching within the target website 420 and/or A web page/toolbar/feature 429 for products other than the one. If the webpage user is registered in the target website 420, and the webpage user logs in to the account when browsing the target website 420, the target website 420 can also provide the webpage user with a shopping cart 428 and a watch list. 424. For example, the shopping cart 428 can be a web page displaying a previous purchase history of the web page user, and the watch list 424 can be a web page presenting a list of products of particular interest to the web user.

The operations of the web pages, functions, toolbars, and/or links of the target website 420 may be supported by various databases stored in the backdoor server 430. For example, each product and product inquiry The information of the answer can be stored in a product database 436 and a product inquiry database 432, respectively. When a web page user browses the product page 426 or the product inquiry page 422, the backdoor server 430 can obtain corresponding information from the product database 436 and/or the product inquiry database 432, and transmit the information to The corresponding web page displayed on the client device 470. The watch list 424 associated with the web page user account and the information of the shopping cart 428 may also be stored in a watch list database 434 and a shopping cart transaction database 438, respectively. Therefore, when the webpage user logs into the online shopping webpage 420 and checks the watch list or historical transaction record of the webpage user in the target website 420, the backdoor server 430 can obtain corresponding information and can transmit the webpage. Information to the corresponding web page.

After the target website 420, the index pre-processing unit 452 can receive and/or collect product information from the databases of the target website 420, such as "product name", "product classification tree" and "transaction history". .

The product referred to in the application of the present invention may be a specific item or service sold by the target website 420. The classification referred to in the application of the present invention may mean a collection of a plurality of things sharing a common attribute. For example, the iPhone ^4TM is a specific item that can be actually sold in the target website 420. Thus, the iPhone ^4TM can be a product as defined by the present application. Similarly, the Samsung Galaxy S4 ^TM can also be a product defined by the present application. "Smartphone" is a concept that refers to any mobile phone that provides more advanced computing power and connectivity than a contemporary feature phone. Therefore, a "smart phone" can be a classification under the definition of the present invention. Because the iPhone 4 ^TM and the Samsung Galaxy S4 ^TM offer more advanced computing power and connectivity than the contemporary feature phone, both are in the same category, which is the smart phone.

A product classification tree is a tree data structure that organizes all categories and sub-categories of products sold by the target website 420. For example, the main classification of the product classification tree may include items such as "items used daily" and "3C". The "3C" category can include sub-categories such as "smart phones," "cameras," "television," and the like. The "smart phone" sub-category may further include sub-categories such as "iPhone", "Nokia", "Sony", "Samsung" and the like.

A transaction log can be a transaction record of a buyer (eg, the web user). It may contain all the necessary details of the transaction made by the buyer during a defined period. For example, a transaction record of a registered web page user can record all historical transactions for all products purchased by the web page user on the target website 420. Each historical transaction record can include the time, price, product, credit card information, and shipping address of the transaction.

The pre-index processing unit 452 can also receive and/or collect historical web activity of the web page user on the target website 402. Such historical online activities may be information reflecting the historical purchase intent of the web user during a defined period (eg, 2 weeks or 14 weeks). For example, the index pre-processing unit 452 can receive and/or collect online purchase history of the webpage user from the shopping cart of the webpage user during the previous two weeks. The pre-index processing unit 452 can also receive and/or collect product names stored in the watch list 424 of the account during the previous two weeks. These product names can reflect products that are of interest to the users of the web page. When the webpage user is a registered member of one of the target websites 420 and has an account number therein, the information can be obtained, and thus has account specificity.

Other historical online activity information, such as the time, length and frequency of the web page user visiting the product page 426, the question left by the web user on the product inquiry page 422 The question/answer/comment, and the words the user of the web page searched on the product search page 429, etc., are not associated with the account of the webpage user and are therefore a general historical online activity. The backdoor server 420 and the index pre-processing unit 452 can also store and associate the user's general online activities by identifying the client device 470 used by the user while browsing the network.

In the case where the target website is based on the "Grid" 440, it is like using Yahoo! The YUI Grids CSS of the ^TM , the purchase intent information of the web page user described above may also be stored in various records in the grid 440. For example, the search history of the web page user may be stored in one of the search records 442 of the web 440, and the browsing history of the web user may be stored in one of the web user's browsing records 444. Digital humanity (DH), which is a data exploration technology that is good at collecting online for large-scale cultural data collection, can be applied to the grid 440 to provide effective data exploration for the historical online activities of the web user. . Therefore, the pre-index processing unit 452 can also receive and/or collect the account-specific and general historical purchase intent information through the grid 440.

In addition to the historical online activities, the pre-index processing unit 452 can also collect and/or receive the web page user on the target website 420 during a defined period (eg, 1 hour, 1 day, or 7 days). Recent online activities. Such recent online activities may be performed during a period that is closer than the historical online activity, and thus may be reflected or highly correlated with the instant purchase intention of the web page user 420 browsing the landing page 420 at the time. News. For example, online activities performed before 1 hour may highly reflect the instant purchase intent of the web page user. The instant purchase intent information may include the search term of the product search page 429 and/or a referral page, and the length of time the web page user spends on a product page 426 during the definition period. Frequency, or the category page to which the product belongs, and a referral page The similarity between the meta-data and the product page 426.

The referral webpage can be an internal webpage that directs the webpage user to a webpage on the target website 420 that is related to the target product. The referral webpage can also be an external webpage 410 that directs the webpage user to the target website 420. For example, if a webpage user is originally browsing the external website 410 before visiting a webpage of the target website 420, and by clicking on one of the links provided by the external website 410, the webpage user is directed to the target website 420. The product page 426 (ie, the external website 410 "refers" the user of the web page to the target website 420), the index pre-processing unit 452 can obtain the user of the web page in the referral website 410. These online activities. The referral website 410 can be a search website 412, such as Yahoo! ^TM search page, provide product information to external sites 414 and / or for the social network 416 (eg, Facebook ^TM) one website, where members will disclose and / or discuss its interest in related products.

If the web page user is directed to the target website 420 from an external website such as one of the referral websites 410, the index pre-processing unit 452 can receive and retrieve the web page user in the referral website 410. The searched search terms, and/or the meta-data of the referral website 410, and the received data are processed in a database stored in the package index 454.

For the recent online activity of the user at the target website 420, the target website 420 can process and transmit the required materials to the pre-index processing unit 452. For example, if the required data is a one-factor value, the target website 420 can calculate the value of the factor based on the online activity record of the web user owned by the target website. Next, the target website 420 can transmit the calculated value of the factor to the pre-index processing unit 452 as a query parameter. The factor may reflect the time spent by the web page user on one of the product pages 426 or a category page 426 to which the product belongs. Length between. The factor may also be reflected in the frequency at which the web page user visits one of the product pages 426 or a category page to which the product belongs. The target website 420 can transmit the calculated value of the factor to the index pre-processing unit 452 through a web communication API such as a PHP communication or a small text file.

Alternatively, the target website 420 can be embedded in an application program interface (API) of the online electronic coupon distribution system 450 and can delegate the calculation to the application. The application can be a web service application interface for asynchronous JavaScript and XML (Asynchronous JavaScript and XML, AJAX) calls and/or JavaScript code. The target website 420 can interface the application to only a particular web page such as the product page 426, or can embed the application interface in all web pages transmitted to the client device 470. The application interface can perform the delegation calculation and transfer the requested data to the index pre-processing unit 452.

In addition to the pre-index processing unit 452, the history and instant purchase intent information of the web page user can also be transmitted to the target server 456 for processing.

The pre-indexing processing unit 452 can periodically collect and/or receive the recent and historical online activities of the web page user at defined time intervals, or collect and/or receive in an instant manner. For example, the pre-index processing unit 452 can collect and/or receive the historical online activities of the web page user from the backdoor server 430 hourly or daily. The index pre-processing unit 452 can collect and/or receive the recent online activities of the web page user from the grid 440 every second, hour, or day.

After receiving and/or collecting the recent and historical online activities of the web page user, the pre-index processing unit 452 can communicate with a parcel index 454 and structure the data. The format transmits the material to the package index 454. For example, the structured data format may be a simple structured data format (SSDF), a tepplot structured data format, a JavaScript Object Notification (JSON) format, or Any other structured format available at the time of filing of the present application or in the future.

The pre-indexing processing unit 452 can also function as an indexer to process and convert the data into an index and transmit the index to the parcel index 454. For example, the pre-index processing unit 452 can be configured to generate an index of one of the contents, such as an index for one or more repositories. The indexed content can contain associated background content and can be searched to find content, including background content. The index can contain a plurality of index items, wherein the index item can be assigned a value, which is a weight. The index item can contain a portion of the database. In some embodiments, the pre-indexing processing unit 452 can use an inversion index that is stored from the content into a repository archive or in a file or collection of files at the location of the content. Mapping relations. The record layer inversion index can include a reference list that is a reference for each text in a plurality of files. A text layer inversion index may additionally include the location of each text in a file. The weight of the index item can be specified. For example, in an exemplary embodiment, a weight may be substantially specified based on the difference between the number of indexed records that do not have the indexed item and the number of indexed records that have the indexed item.

Alternatively, the package index 454 can receive the original data of the recent and historical online activities of the web page user and process the data into a structured data format or index.

The package index 454 can communicate with the target server 456 to transmit the required data of the recent and historical online activities of the web page user to the target server 456. Target servo The 456 may include a cache mechanism (eg, STcache or memcache) to query the data stored in the package index 454. The data can also be transmitted to a (not shown) search engine (e.g., Vespa) that can provide the target server 456 with a high performance query interface.

The target server 456 can communicate with an electronic coupon database 460 from which the target server 456 can select electronic coupons for various products sold by the target website 420. The target server 456 can then transmit a selected electronic coupon to a web page (eg, the product page 426) displayed by the target website 420 on the client device 470.

When the webpage user is accessing a webpage of the target website 420, the target server 456 can receive the historical and recent online activity information of the webpage user from the search engine or the parcel index 454. The target server 456 can then convert the historical and recent online activity information into purchase intent information associated with a target product. Alternatively, the history and recent purchase intent information may also be converted and stored in the package index 454 before the web user browses the target website 420.

Using the purchase intent (PI) information, the target server 456 can determine the current intent of the web page user to purchase a target product. If the webpage user's purchase intention for the target product is greater than a threshold, the target server 456 may select an electronic coupon of the target product from the electronic coupon database 460 and transmit the electronic coupon to the use. The page that the user is browsing. The electronic coupon can be a pop-up window, banner or link that appears on the web page.

The fifth figure is a flow diagram of a method 500 of immediately assigning an electronic coupon to a web page user in accordance with an exemplary embodiment of the present invention. The method may require a server having a non-transitory computer readable storage that stores a set of instructions for online electronic coupon distribution. Save the media. The set of instructions can include a set of logic instructions and/or computer commands to perform the online electronic coupon distribution. For example, each step in the fifth figure may be one of logic instructions and/or computer commands or a set of logic instructions and/or computer commands for performing the online electronic coupon distribution. The server can also have a processor in communication with the non-transitory computer readable storage medium, the processor being configured to execute the set of instructions stored in the computer readable storage medium. In an exemplary embodiment, the server can be the server 300 of the third figure. The server can also be the target server 456 in the fourth figure.

The method can be implemented by any other online sales promotion system at the time of filing of the present application or in the future, but for purposes of description, the method is applied to the online electronic coupon distribution system 450 shown in the fourth figure.

In step 510, the processor of the target server 456 can receive historical purchase intent information associated with a web page user and a target product.

In step 520, the processor of the target server 456 can determine a plurality of factors. These factors can be associated with a registered account of one of the users and reflect the historical purchase intent of the web user.

For example, the processor can determine a factor F1 that is related to the purchase history of the webpage user in the shopping cart 426 by the webpage user during a defined period (eg, 2 weeks, 4 weeks, or 14 weeks). . The factor F1 may reflect whether the webpage user has previously purchased the target product, or whether the webpage user has previously purchased a product having the same classification as the target product. This factor F1 can have a value between 0 and 1. For example, if the webpage user purchases a target product within 4 weeks, F1=1; if the webpage user purchases the product having the same classification as the target product within 4 weeks, F1=0.5; The webpage user is within 4 weeks F1=0 if you have not purchased the target product or purchased a product with the same classification as the target product.

The processor may also determine a factor F2 associated with the watch list 424 of the web page user in the target website 420. The factor F2 may reflect whether the watch list 424 contains the target product or a product having the same classification as the target product during a defined period (eg, 2 weeks, 4 weeks, or 14 weeks). This factor F2 can have a value between 0 and 1. For example, if the webpage user includes the target product in the watch list within 4 weeks, F2=1; if the watch list includes products having the same classification as the target product within 4 weeks, F2=0.5; If the target product is not included in the watch list within 4 weeks and the product with the same classification as the target product is not included, then F2=0.

Both F1 and F2 reflect the user's historical purchase intent associated with the registered user's account, and therefore have account specificity.

In step 530, the processor of the target server 456 can determine a plurality of factors that reflect the user's general historical purchase intent, that is, historical purchase intent that is not associated with the user's registered account.

For example, the processor can determine a factor F3 that is associated with the product query page 422 of the target product. The factor F3 may reflect whether the web page user has viewed, asked questions, answered questions and/or comments on the product inquiry page 422 during a defined period (eg, 2 weeks, 4 weeks, or 14 weeks). The factor F3 can have a value between 0 and 1. For example, if the webpage user asks a question in the product inquiry page 422 of the target product within 4 weeks, F3=1; if the webpage user has the product of the same classified product in the target product within 4 weeks If the question is asked in the inquiry page 422, then F3=0.75; if the user of the webpage browses After viewing the product inquiry page 422 of the target product, F3=0.5; if the web page user browses the product inquiry page 422 of the product having the same classification as the target product, F3=0.25; and if the user The product inquiry page related to the target product or the classification of the target product has not been reviewed or participated, and in any discussion, F3=0.

The processor of the target server 456 can also determine a factor F4 that reflects whether the user has viewed the product page 426 during a defined period (eg, 2 weeks, 4 weeks, or 14 weeks). This factor F4 can have a value between 0 and 1. For example, if the web page user views the product page of the target product 426 n times (n≧1) within 4 weeks, if n>3, then F4=1; if 2≦n≦3, then F4=0.75 ; if n=1, then F4=0.6

If the web page user has viewed 426n times (n≧1) of the product page having the same classification as the target product, if n>3, then F4=0.5; if 2≦n≦3, then F4=0.35; If n=1, then F4=0.25; if the web page user has not viewed the target product and/or product page 426 having one of the same categories as the target product, then F4=0.

The processor of the target server 456 can also determine a factor F5 that reflects the user's search for keywords associated with the target product during a defined period (eg, 2 weeks, 4 weeks, or 14 weeks). , the number of pages viewed. This factor F5 can have a value between 0 and 1. For example, if within 4 weeks, the web user searches for the target product or the item One of the keywords of the target product, and clicked on n result links, if n>3, then F5=1; if 2≦n≦3, then F5=0.75; if n=1, then F5=0.5; others In the case, F5=0.

In addition, the processor of the target server 456 can also determine a factor F9 that reflects whether the web page user is different from the referral website during a defined period (eg, 2 weeks, 4 weeks, or 14 weeks). On the external website, expressed a special interest in the target product. For example, the web page user can be a registered member of an external website (eg, a social networking website) on which to discuss issues related to the target product with his/her friends. This factor F9 can have a value between 0 and 1. For example, if the web user has discussed the target product with other people in the external website within 4 weeks, F9=1; if the web user has discussed with other people in the external website, the target product has F9=0.5 for a product-related issue of the same category; and F9=0 if the page user has never discussed any issues related to the target product. The factor F9 can be a selectivity factor that determines the historical purchase intent of the web page user.

F3, F4, F5, and F9 are factors that reflect the general purchase intention of the web page user, that is, they are not related to a particular registered account of the web page user. In addition, F1, F2, F3, F4, F5, and F9 may be factors reflecting the purchase intention of the web page user for a relatively long period of time (for example, 2 weeks, 4 weeks, or 14 weeks), so it is a historical purchase. Intention factor.

At step 540, the processor of the target server 456 can receive a plurality of factors reflecting the user's instant purchase intent, the factors being determined by the target website 420 or by an application interface embedded therein.

For example, the processor of the target server 456 can receive a factor F6 that reflects that the target product and the user have submitted to a search engine during a defined period (eg, 1 hour, 1 day, or 7 days). The association between one or more words. This factor F6 can have a value between 0 and 1. The search engine can be a referral website. For example, the target server 456 can receive a plurality of search terms searched by the webpage user within one hour from a plurality of referral websites or links (eg, from the referral website 410), and search for the searches. The words are compared to a plurality of keywords stored in a keyword database (not shown) of the package index 454. If a search term matches the target product name, then F6=1; if the search term matches one of the target product related keywords, then F6=0.9; if the search term belongs to the target product If the classification matches, then F6=0.8; if the search term matches one of the related keywords of the classification to which the target product belongs, F6=0.7; if it is any other case, then F6=0.

The processor of the target server 456 can receive a factor F7 that reflects a plurality of web pages associated with the target product during a defined period (eg, 1 hour, 1 day, or 7 days). The length of time and the frequency with which the web user searches or views multiple web pages associated with the target product. For example, the web pages may be the product page 426 of the target product and the product inquiry page 422 and/or the classification to which the target product belongs. This factor F7 can have a value between 0 and 1. For example, within 5 hours, if the web page user switches between a web page not related to the target product and a web page related to the target product (such as the product page 426 and the product inquiry page 422) n Times (n>1), and the total time spent by the web page user to view these web pages is m minutes (m>10), if n>3, m>10, then F7=1; if 2≦n≦3 , m>10, then F7=0.9; If n=1, m>10, then F7=0.8; if the webpage user switches n times (n>1) between the product page 426 and the product inquiry page 422 having one product of the same category as the target product. And the total time spent by the webpage user for viewing the webpages is m minutes (m>10), if n>3, m>10, then F7=0.7; if 2≦n≦3, m>10, then F7 = 0.6; and if n = 1, m > 10, then F7 = 0.5.

If it is any other case, then F7=0.

The processor of the target server 456 can receive a factor F8 that reflects the external referral pages 412, 414 that the web page user views during a defined period (eg, 1 hour, 1 day, or 7 days). The similarity between the metadata of 416 and the metadata of the webpages of the target website 420. This factor F8 can have a value between 0 and 1. For example, the web page user has viewed the product page 426 within 1 hour. Accordingly, if the metadata of the referral webpage 412, 414, 416 is the same as the meta-information of the target product page 426, that is, when the referral webpage 412, 414, 416 is an external webpage of the target product, Then, F8=1; if the metadata of the referral page matches the related keyword of the target product stored in the keyword database of the package index 454, then F8=0.8; if the referral page 412 If the meta-data of 414, 416 is the same as the classification of the target product, then F8=0.6; if the meta-data of the referral webpage, and the keyword database stored in the parcel index 454, the classification of the target product When a related keyword matches, F8=0.5; if it is other, F8=0.

F6, F7 and F8 are factors reflecting the general purchase intention of the web page user, that is, they are not related to the specific registered account of the user. In addition, F6, F7 and F8 phases Relative to the factors F1-F5, a factor reflecting the purchase intention of the webpage user in a relatively recent period (for example, 1 hour, 1 day, or 7 days), so it is highly indicative of the webpage The immediate purchase intent factor of the user's current purchase intention.

At step 550, the processor of the target server 456 can determine a PI score based on the purchase intention factors (eg, F1-F8), the purchase intention score reflecting the user's association with the target product. A current purchase intention. The purchase intention score can be expressed as: Where Fi is a purchase intention factor and ai is a weight that represents the importance of the purchase intent factor Fi in determining the purchase intention score.

If the web page user is a registered user and logs in to his/her registered account in the target website 420, the target server 456 can identify the user's identity through his/her account. It is therefore possible to receive account specific information for the user. Accordingly, if each purchase intention factor is considered to have the same importance, that is, ai =1, the purchase intention score of the web page user can be expressed as follows: PIscore = ( F 1+ F 2+ F 3+ F 4+ F 5+ F 6+ F 7+ F 8+ F 9)/9

When the selective purchase intention factor F9 is not considered, the purchase intention score of the web page user can be expressed as follows: PIscore = ( F 1+ F 2+ F 3+ F 4+ F 5+ F 6+ F 7+ F 8)/8

If the webpage user does not log in to his/her account when browsing the target website 420, the target server 456 may not be able to receive the account specific information of the webpage user, but the client device of the webpage user may still be recognized. 470. Thus, the target server 456 may be able to determine the general historical purchase intent factors and the instant purchase intent factors of the web page user. Accordingly, if each purchase intention factor is considered to have the same importance, that is, ai =1, the purchase intention score of the web page user can be expressed as follows: PIscore = ( F 3+ F 4+ F 5+ F 6+ F 7+ F 8+ F 9)/7

When the selective purchase intention factor F9 is not considered, the purchase intention score of the web page user can be expressed as follows: PIscore = ( F 3+ F 4+ F 5+ F 6+ F 7+ F 8)/6

Since each purchase intention factor F1-F9 is adjusted to a value between 0 and 1, the purchase intention score is also a value between 0 and 1.

At step 560, the processor of the target server 456 can compare the purchase intent score with a threshold (ie, a threshold value). If the purchase intent score is greater than the threshold, the web page user can be considered to have a strong current purchase intent for the target product. The target server 456 can communicate with the electronic coupon database 460, select one or more electronic coupons associated with the target product, and transmit the selected electronic coupons to the webpage user through the client. The web page that the end device 470 is browsing. The electronic coupon may be a pop-up window, banner, link, a message transmitted to the web user's registered account, and/or any other suitable form appearing on the web page. The critical point value can be 0.5, or can be determined using artificial intelligence (AI) or fuzzy data models that implement machine learning programs.

As described above, such systems and computer implementations provide current online purchase intent calculations for online store owners, platform providers, manufacturers, and/or vendors in an instant online electronic coupon distribution. In some embodiments, the system and method can calculate a web user for a web page user based on a general online activity associated with a target product. The purchase intention score of the target product. In other embodiments, the system and method can calculate the purchase intention score based on account-specific activity information and the general online activities of the web user associated with a target product.

However, the foregoing detailed description is to be considered as illustrative and not restrictive, and the scope of the invention is defined by the scope of the appended claims (including all equivalents thereof).

For example, while the above systems and methods are described with respect to online electronic coupon distribution, it should be understood that the same systems and methods can be implemented in other forms of online sales promotions, such as discounts, samples, competitive promotions, loyalty rewards.

Moreover, while the above described systems and methods are described with respect to the distribution of online electronic coupons, it should be understood that the same systems and methods can be implemented for online advertising displays.

In addition, while the specific example embodiments have been shown and described with reference to the first to fifth figures, it will be understood by those of ordinary skill in the art without departing from the exemplary embodiments as defined by the following claims Various changes in form and detail can be made in the spirit and scope. Accordingly, the exemplary embodiments are provided by way of example only, and are not intended to be As such, the invention is intended to have a broad and broad scope. For example, the subject matter of the invention may be embodied as a method, apparatus, component or system, among other things. Accordingly, particular embodiments may take the form of, for example, a hardware, a soft body, a firmware, or any combination thereof. Therefore, the detailed description herein should not be construed as limiting.

In the context of this specification and the claims, the words may have more than a clearly stated meaning, and have a subtle meaning implied or suggested in the text. The same, used here The phrase "in a particular embodiment" or "in an exemplary embodiment" does not need to refer to the same embodiment, and the phrase "in another embodiment" or "in another embodiment" In an exemplary embodiment, it is not necessary to refer to a different embodiment. For example, the claimed subject matter is intended to encompass a combination of the specific embodiments.

The terminology used in the specification is for the purpose of the description In general, terms can be understood at least in part from the use of context. For example, the words "and", "or" or "and/or" used herein may include various meanings depending, at least in part, on the context in which the word is used. In general, if "or" is associated with a list such as A, B or C, it is intended to be used in the meaning of inclusion, meaning A, B and C, and in the sense of mutual exclusion Line means A, B or C. In addition, the term "a" or "an" or "an" or "an" The combination. Similarly, it can be understood that the words "a", "an" or "the" can also be used in the singular or plural. In addition, it should be understood that the term "subject" does not necessarily mean a mutually exclusive set of multiple factors. Instead, other factors that are not explicitly recited may be allowed, at least in part, depending on the context.

Similarly, it will be understood that when a component is "connected" or "connected" to another component, it can be directly connected or connected to the other component or a plurality of intervening components. In contrast, when an element is referred to as being "directly connected" or "directly connected" to another element, there are no other intervening elements. Other words used to describe the relationship between multiple components should also be interpreted in the same form (for example, "between" and "directly between", "adjacent" and "directly adjacent" Yu "etc."

To further understand the use of the terms "including", "comprising", "including", "including", "the" The existence of one or more additional features, aspects, steps, operations, components, components, and/or clusters thereof are not excluded, and in the description herein, the same reference numerals are used to refer to the same elements.

500‧‧‧ method

510-560‧‧‧Steps

Claims (23)

A computer server includes: a non-transitory computer readable storage medium comprising a set of instructions for online sales promotion; a processor, the processor and the non-transitory computer Reading storage medium communication, the processor being configured to execute the set of instructions stored in the computer readable storage medium to: receive purchase intent information associated with a user and a target product, and when the user is in a customer When the terminal device visits a target website, determining the purchase intention information from the plurality of purchase intention factors, wherein the determining the plurality of purchase intention factors comprises: based on a first predefined period of time before a current time The information of a registered account registered by the user on the target website determines a first factor to reflect the historical purchase intention of the user associated with the registered account of the user; based on a second before the current time Determining a second factor from the information in the registered account for the user in the predefined period to reflect the note not with the user a historical purchase intention of the user associated with the account; and determining a third factor to reflect the immediate purchase intention of the user based on the information in a third predefined period before the current time, the third predefined period Substantially shorter than the first predefined period and the second predefined period; determining a purchase intention score according to the at least the first, the second, and the third purchase intention factor, the purchase intention score reflecting the target The current purchase intention of the user associated with the product; and providing the target with the determined purchase intention score exceeding a predetermined value One of the product associations sells online to a target webpage of the target website for presentation to the user of the client device. The computer server of claim 1, wherein the first factor is associated with at least one of: an account associated with the user indicates whether the user has previously purchased the target product, or has previously A product that has been classified in the same classification as the target product; or whether a watch list of the user includes the target product or includes a product of the same classification as the target product. The computer server of claim 1, wherein the second factor is associated with at least one of: whether the user has viewed a product inquiry webpage associated with the target product during a defined period; Whether a user has viewed a web page of the target product during a defined period; the number of web pages viewed by the user when searching for keywords related to the target product; or from the user's general internet The personal interest information associated with the target product as reflected in the road activity. The computer server of claim 1, wherein the third factor is associated with at least one of: the target product and one or more of the user submitted to the search engine during a defined period of time The association between words; the length of time it takes for the user to browse the web page associated with the target product, and During a defined period, the user searches for or views a frequency of a webpage associated with the target product; or, during a defined period, directs the user to a meta-material of a referral webpage of the current webpage, and uses the same The meta-information of the current web page as viewed by the person, the similarity between the two. The computer server of claim 1, wherein the processor is further configured to: periodically receive, from a scripting language embedded in the target website, the user associated with the target product The third factor, wherein the third factor is calculated based on the immediate purchase intention of the user. The computer server of claim 1, wherein the processor is further configured to: integrate an index pre-processing component into the target website; and when the user logs in to the target website, The pre-index processing component periodically receives the historical purchase intent associated with the registered account from the target website; and calculates the first factor based on the historical purchase intent associated with the registered account. The computer server of claim 1, wherein the processor is further configured to: integrate an index pre-processing component into the target website; periodically retrieve the target website through the index pre-processing component Receiving the historical purchase intent not associated with the registered account; and calculating the second factor based on the historical purchase intent not associated with the registered account. The computer server according to claim 1, wherein the online sales promotion is one An electronic coupon that expires within 2 hours. The computer server according to claim 1, wherein the determination of the purchase intention score is based on at least one of the first factor, the second factor, and the third factor, including weighting processing each of the purchase intentions The factor, a sum of the purchase intention factor after the weighted processing, and a quantity of the purchase intention factor divided by a quantity. A computer implementation method for online sales promotion, the method comprising: receiving, by a computer processor, purchase intention information associated with a user and a target product, and when the user visits a client device Determining the purchase intention information from the plurality of purchase intention factors, wherein determining the plurality of purchase intention factors comprises: based on the target from the user in a first predefined period before a current time Information of a registered account registered by the website, determining a first factor to reflect the historical purchase intention of the user associated with the registered account of the user; based on a second predefined period prior to the current time The information in the registered account of the user determines a second factor to reflect the historical purchase intention of the user not associated with the registered account of the user; and based on a third predefined before the current time The information in the period determines a third factor to reflect the user's immediate purchase intention, the third predefined period Substantially shorter than the first predefined period and the second predefined period; determining, by a computer processor, a purchase intention score based on at least one of the first, second, and third purchase intention factors, the purchase intention The score reflects the current purchase intent of the user associated with the target product; When the determined purchase intention score exceeds a predetermined value, a computer processor provides an online sales promotion associated with the target product to a target webpage of the target website for presentation to the user of the client device. The computer-implemented method of claim 10, wherein the first factor is associated with at least one of: an account associated with the user indicates whether the user has previously purchased the target product, or has previously A product that has been classified in the same classification as the target product; or whether a watch list of the user includes the target product or includes a product of the same classification as the target product. The computer-implemented method of claim 10, wherein the second factor is associated with at least one of: whether the user has viewed a product inquiry webpage associated with the target product during a defined period; Whether a user has viewed a web page of the target product during a defined period; the number of web pages viewed by the user when searching for keywords related to the target product; or from the user's general internet The personal interest information associated with the target product as reflected in the road activity. The computer-implemented method of claim 10, wherein the third factor is associated with at least one of: the target product and one or more of the user's submitted to a search engine during a defined period Relevance between words; a length of time taken by the user to browse a web page associated with the target product, and a frequency at which the user searches or views a web page associated with the target product during a defined period; or during a defined period of time The similarity between the meta-information of the user to a referral page of the current webpage and the meta-information of the current webpage viewed by the user. The computer implementation method of claim 10, further comprising: periodically receiving, by a computer processor, a script language embedded in the target website, the user associated with the target product A third factor, wherein the third factor is calculated based on the instant purchase intention of the user. The computer implementation method of claim 10, further comprising: integrating, by a computer processor, an index pre-processing component into the target website; when the user logs in to the target website, processing by a computer The historical purchase intent associated with the registered account is periodically received from the target website through the index pre-processing component; and the first factor is calculated by a computer processor based on the historical purchase intent associated with the registered account. The computer implementation method of claim 10, further comprising: integrating, by a computer processor, an index pre-processing component into the target website; and periodically, the computer processor passes the index pre-processing component The target website receives a historical purchase intent that is not associated with the registered account; and the second factor is calculated by a computer processor based on the historical purchase intent not associated with the registered account. The computer implementation method of claim 10, wherein the online sales promotion is an electronic coupon that expires within 2 hours. The computer-implemented method of claim 10, wherein the determination of the purchase intention score is based on at least one of the first factor, the second factor, and the third factor, including weighting processing each of the purchase intentions The factor, a sum of the purchase intention factor after the weighted processing, and a quantity of the purchase intention factor divided by a quantity. A non-transitory computer readable storage medium comprising a set of instructions for conducting an online sales promotion, the set of instructions directing a computer processor to perform the following actions; receiving and a user and a a purchase intention information associated with the target product, and when the user visits a target website on a client device, determining the purchase intention information from the plurality of purchase intention factors, wherein determining the plurality of purchase intention factors comprises: based on Determining a first factor to reflect the user associated with a registered account of the user from information about a registered account registered with the user on the target website in a first predefined period before the current time a historical purchase intent; determining a second factor from the information in the registered account regarding the user based on information in the second predetermined period before the current time to reflect that the registered account is not associated with the user The user's historical purchase intent; and based on information in a third predefined period prior to the current time, a third factor to reflect an immediate purchase intention of the user, the third predefined period being substantially shorter than the first predefined period and the second predefined period; according to at least one of the first, the second, and the The third purchase intention factor determines a purchase An intention score, the purchase intention score reflecting a current purchase intention of the user associated with the target product; and providing an online sales promotion associated with the target product to the online purchase intention based on the determined purchase intention score exceeding a predetermined value A target web page of the target website is presented to the user of the client device. The non-transitory computer readable storage medium of claim 19, wherein: the first factor is associated with at least one of: an account associated with the user indicates whether the user has previously purchased the a target product, or a product that has previously been purchased in the same classification as the target product; or whether a watch list of the user includes the target product, or includes a product of the same classification as the target product; the second factor and the following At least one of the associations: whether the user has viewed a product inquiry page associated with the target product during a defined period; whether the user has viewed a web page of the target product during a defined period; The number of pages viewed by the user when the keyword is related to the target product; the personal interest information associated with the target product reflected from the user's general internet activity; and the third factor and At least one of the following: during a defined period, the target product and the user have been submitted to a search engine The association between one or more words: the length of time it takes for the user to browse the web page associated with the target product, and during a defined period, the user searches or views the web page associated with the target product a frequency; or a similarity between the metadata of a referral page of the current web page and the meta-data of the current web page viewed by the user during a defined period. The non-transitory computer readable storage medium of claim 19, wherein the set of instructions is further configured to direct the computer processor to further perform the following actions: periodically from a scripting language embedded in the target website ( Script) receiving the third factor of the user associated with the target product, wherein the third factor of the user is calculated according to the instant purchase intention of the user. The non-transitory computer readable storage medium of claim 19, wherein the set of instructions is further configured to direct the computer processor to further perform the following actions: integrating an index pre-processing component into the target website; The index pre-processing component periodically receives from the target website: the historical purchase intent not associated with the registered account; and the historical purchase intent associated with the registered account when the user logs into the target website; Calculating the first factor based on the historical purchase intent associated with the registered account; and calculating the second factor based on the historical purchase intent not associated with the registered account. A non-transitory computer readable storage medium as described in claim 19, wherein Determining the first factor, the second factor, and the third factor, the purchase intention score is determined by including a weighting process for each of the purchase intention factors, a sum of the purchase intention factors after calculating the weighting process, and Divide by a quantity of this purchase intention factor.